Coupling isoprene and monoterpene emissions from Amazonian tree species with physiological and environmental parameters using a neural network approach

Abstract

The ability to predict isoprene emissions from plants is important for predicting atmospheric chemistry. To improve the basis for prediction capability, data obtained from continuous field measurements of isoprene and monoterpene emissions from three Amazonian tree species were related to observed environmental and leaf physiological parameters using a new neural network approach. The environmental parameters included leaf temperature, light, relative humidity, water vapour pressure deficit, and the history of ambient temperature and ozone concentration, whereas the physiological parameters included stomatal conductance, assimilation and intercellular CO2 concentration. The neural approach with 24 different combinations of these parameters was applied to predict the emission variability observed during short time periods (2-3 d) with individual tree branches and, on a longer-term scale, in aggregated data sets from different seasons, leaf developmental stage, and light environment. The results were compared to the quasi standard emission algorithm for isoprene. On the short-term scale, good agreement (r2 ≈ 0.9) was obtained between observations and predictions of the standard algorithm as well as predictions of the neural network using the same input parameters (leaf temperature and light). When these predictors were used to model the long-term emission variability, r2 was reduced to < 0.5 for both approaches. Remarkably, for the neural technique, more than 50% of the unexplained variance could be explained by the mean temperature of the preceding 36 h. An even better network performance was obtained with physiological parameter combinations (r2 > 0.9) suggesting a strong and applicable link between isoprenoid emission and leaf primary metabolism. © 2005 Blackwell Publishing Ltd.